Machine learning (ML) has revolutionized healthcare. Machine learning technology facilitates healthcare providers to analyze numerous different data points and propose outcomes. According to a study published by JAMIA, machine learning can be used to extract social determinants of health (SDOH) data from Electronic Medical Records (EMR) Software clinical notes. This extraction of data can help in the development of clinical decision support systems.
The Importance of SDOH Data
Social determinants of health data can greatly impact health outcome levels. Healthcare providers who want to deliver optimum care quality need to consider other factors that have a direct impact on the patient’s health. These elements comprise income, an individual’s access to care, and dietary consumption, which comprise social determinants of health. SDOH provides insights related to non-clinical factors that influence a patient’s well-being.
It’s difficult to extract SDOH data as the information is not easily accessible, particularly when the provider is working on treatment plans. SDOH data is in Electronic Health Records (EHR) software systems but are unstructured text within clinical notes, patient data, and patient portal EMR software messages.
It is estimated that 80% of clinical data is stored in an unstructured format which makes it difficult to access and use. Hence, clinicians might be unacquainted with the SDOH data which can impact provider’s decision-making and can hurt patient health outcome levels.
Steps Involved in Extracting SDOH Data from EMR Software Clinical Notes using ML
There are multiple phases involved in utilizing machine learning to extract SDOH information from clinical notes in Electronic Health Records (EHR) Software:
- Data Collection and Preprocessing
- Annotation and Labeling
- Feature Engineering
- Model Selection and Training
- Evaluation and Validation
- Integration and Deployment
- Monitor the Model’s Performance
How can Machine Learning help to make SDOH data accessible in EMR Software Clinical Notes?
Machine learning and natural language processing can be used to open SDOH data from EHR software systems. This provides a complete picture of each patient’s healthcare conditions. A query can be promptly created by the user to extract main conceptions from unstructured patient data. This helps to detect issues that impact patient health and outcomes.
This data can then be used with analytic tools such as machine learning algorithms. When the SDOH data is identified and made accessible healthcare providers can easily introduce new patient care plans and make any other changes that can have a positive impact on patient outcome levels and facilitate high-quality care.
The Final Results
With the help of machine learning technology healthcare providers can identify patients that are at risk of poor outcome levels due to social determinants of health problems. Using this meaningful information at hand, clinicians can take quick measures to link patients with valuable resources. These might include financial aid for important medication, chronic disease management through education resources, and improving access to screenings. Taking proactive steps using SDOH data in EMR Software can greatly improve patient care and doctors can feel confident about their care plans and patient diagnosis.
